MixRHLP_Matlab_v1
This toolbox contains the codes of the expectation-maximization algorithms to infer the mixture models for functional data (time series) clustering and optimal segmentation: mixture of regression models with hidden logistic processes.
For more details, see the papers mentioned in the references sections.
Additional toolboxes for time series segmentation and clustering, unsupervised learning of mixture models will also be provided soon.
引用格式
Faicel Chamroukhi (2024). MixRHLP_Matlab_v1 (https://github.com/fchamroukhi/MixRHLP_m), GitHub. 检索来源 .
@article{Chamroukhi-RHLP-2009, Author = {Chamroukhi, F. and Sam\'{e}, A. and Govaert, G. and Aknin, P.}, Journal = {Neural Networks}, Number = {5-6}, Pages = {593--602}, Publisher = {Elsevier Science Ltd.}, Title = {Time series modeling by a regression approach based on a latent process}, Volume = {22}, Year = {2009} } @article{Chamroukhi-MixRHLP-2011, Author = {Sam{\'e}, A. and Chamroukhi, F. and Govaert, G{\'e}rard and Aknin, P.}, Issue = 4, Journal = {Advances in Data Analysis and Classification}, Pages = {301--321}, Publisher = {Springer Berlin / Heidelberg}, Title = {Model-based clustering and segmentation of time series with changes in regime}, Volume = 5, Year = {2011} } @article{Chamroukhi-RHLP-FLDA, Author = {Chamroukhi, F. and Sam\'{e}, A. and Govaert, G. and Aknin, P.}, Journal = {Neurocomputing}, Number = {7-9}, Pages = {1210--1221}, Title = {A hidden process regression model for functional data description. Application to curve discrimination}, Volume = {73}, Year = {2010} } @article{Chamroukhi-FMDA-2013, Author = {Chamroukhi, F. and Glotin, H. and Sam{\'e}, A.}, Journal = {Neurocomputing}, Pages = {153-163}, Title = {Model-based functional mixture discriminant analysis with hidden process regression for curve classification}, Volume = {112}, Year = {2013} } @article{Chamroukhi-FDA-2018, Journal = {}, Author = {Faicel Chamroukhi and Hien D. Nguyen}, Volume = {}, Title = {Model-Based Clustering and Classification of Functional Data}, Year = {2018}, note={arXiv:1803.00276v2} }
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版本 | 已发布 | 发行说明 | |
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1.0.0 |
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